OpenRaiser/Intern-Atlas huggingface dataset:
Improved Approximate EFX Guarantees for Multigraphs
Venue: arXiv cs.GT 2025
Year: 2025

In recent years, a new line of work in fair allocation has focused on EFX allocations for \((p, q)\)-bounded valuations, where each good is relevant to at most \(p\) agents, and any pair of agents share at most \(q\) relevant goods. For the case \(p = 2\) and \(q = \infty\), such instances can be equivalently represented as multigraphs whose vertices are the agents and whose edges represent goods, each edge incident to exactly the one or two agents for whom the good is relevant. A recent result of \citet{amanatidis2024pushing} shows that for additive $(2,\infty)$ bounded valuations, a \((\nicefrac{2}{3})\)-EFX allocation always exists. In this paper, we improve this bound by proving the existence of a \((\nicefrac{1}{\sqrt{2}})\)-\(\efx\) allocation for additive \((2,\infty)\)-bounded valuations.
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Venue: arXiv cs.CR 2025
Year: 2025

While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce synthetic trajectories. However, current models lack formal privacy guarantees and rely on conditional information derived from real data during generation. This work investigates the utility cost of enforcing DP in such models, addressing three research questions across two datasets and eleven utility metrics. (1) We evaluate how DP-SGD, the standard DP training method for deep learning, affects the utility of state-of-the-art generative models. (2) Since DP-SGD is limited to unconditional models, we propose a novel DP mechanism for conditional generation that provides formal guarantees and assess its impact on utility. (3) We analyse how model types - Diffusion, VAE, and GAN - affect the utility-privacy trade-off. Our results show that DP-SGD significantly impacts performance, although some utility remains if the datasets is sufficiently large. The proposed DP mechanism improves training stability, particularly when combined with DP-SGD, for unstable models such as GANs and on smaller datasets. Diffusion models yield the best utility without guarantees, but with DP-SGD, GANs perform best, indicating that the best non-private model is not necessarily optimal when targeting formal guarantees. In conclusion, DP trajectory generation remains a challenging task, and formal guarantees are currently only feasible with large datasets and in constrained use cases.
Towards Efficient and Effective Alignment of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) exhibit remarkable capabilities across diverse tasks, yet aligning them efficiently and effectively with human expectations remains a critical challenge. This thesis advances LLM alignment by introducing novel methodologies in data collection, training, and evaluation. We first address alignment data collection. Existing approaches rely heavily on manually curated datasets or proprietary models. To overcome these limitations, we propose Lion, an adversarial distillation framework that iteratively refines training data by identifying and generating challenging instructions, enabling state-of-the-art zero-shot reasoning. Additionally, we introduce Web Reconstruction (WebR), a fully automated framework that synthesizes instruction-tuning data directly from raw web documents, significantly improving data diversity and scalability over existing synthetic data methods. Next, we enhance alignment training through novel optimization techniques. We develop Learning to Edit (LTE), a framework that enables LLMs to efficiently integrate new knowledge while preserving existing information. LTE leverages meta-learning to improve both real-time and batch knowledge updates. Furthermore, we introduce Bridging and Modeling Correlations (BMC), a refinement of Direct Preference Optimization (DPO) that explicitly captures token-level correlations in preference data, leading to superior alignment across QA and mathematical reasoning tasks. Finally, we tackle the challenge of evaluating alignment. Existing benchmarks emphasize response quality but overlook adherence to specific constraints. To bridge this gap, we introduce FollowBench, a multi-level, fine-grained benchmark assessing LLMs' ability to follow complex constraints across diverse instruction types. Our results expose key weaknesses in current models' constraint adherence, offering insights for future improvements.
RePO: Replay-Enhanced Policy Optimization
Venue: arXiv cs.CL 2025
Year: 2025

Reinforcement learning (RL) is vital for optimizing large language models (LLMs). Recent Group Relative Policy Optimization (GRPO) estimates advantages using multiple on-policy outputs per prompt, leading to high computational costs and low data efficiency. To address this, we introduce Replay-Enhanced Policy Optimization (RePO), which leverages diverse replay strategies to retrieve off-policy samples from a replay buffer, allowing policy optimization based on a broader and more diverse set of samples for each prompt. Experiments on five LLMs across seven mathematical reasoning benchmarks demonstrate that RePO achieves absolute average performance gains of $18.4$ and $4.1$ points for Qwen2.5-Math-1.5B and Qwen3-1.7B, respectively, compared to GRPO. Further analysis indicates that RePO increases computational cost by $15\%$ while raising the number of effective optimization steps by $48\%$ for Qwen3-1.7B, with both on-policy and off-policy sample numbers set to $8$. The repository can be accessed at https://github.com/SihengLi99/RePO.
"I Said Things I Needed to Hear Myself": Peer Support as an Emotional, Organisational, and Sociotechnical Practice in Singapore
Venue: arXiv cs.HC 2025
Year: 2025

Peer support plays a vital role in expanding access to mental health care by providing empathetic, community-based support outside formal clinical systems. As digital platforms increasingly mediate such support, the design and impact of these technologies remain under-examined, particularly in Asian contexts. This paper presents findings from an interview study with 20 peer supporters in Singapore, who operate across diverse online, offline, and hybrid environments. Through a thematic analysis, we unpack how participants start, conduct, and sustain peer support, highlighting their motivations, emotional labour, and the sociocultural dimensions shaping their practices. Building on this grounded understanding, we surface design directions for culturally responsive digital tools that scaffold rather than supplant relational care. Drawing insights from qualitative accounts, we offer a situated perspective on how AI might responsibly augment peer support. This research contributes to human-centred computing by articulating the lived realities of peer supporters and proposing design implications for trustworthy and context-sensitive AI in mental health.
CoLMbo: Speaker Language Model for Descriptive Profiling
Venue: arXiv cs.CL 2025
Year: 2025

Speaker recognition systems are often limited to classification tasks and struggle to generate detailed speaker characteristics or provide context-rich descriptions. These models primarily extract embeddings for speaker identification but fail to capture demographic attributes such as dialect, gender, and age in a structured manner. This paper introduces CoLMbo, a Speaker Language Model (SLM) that addresses these limitations by integrating a speaker encoder with prompt-based conditioning. This allows for the creation of detailed captions based on speaker embeddings. CoLMbo utilizes user-defined prompts to adapt dynamically to new speaker characteristics and provides customized descriptions, including regional dialect variations and age-related traits. This innovative approach not only enhances traditional speaker profiling but also excels in zero-shot scenarios across diverse datasets, marking a significant advancement in the field of speaker recognition.
Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Kinematic retargeting from human hands to robot hands is essential for transferring dexterity from humans to robots in manipulation teleoperation and imitation learning. However, due to mechanical differences between human and robot hands, completely reproducing human motions on robot hands is impossible. Existing works on retargeting incorporate various optimization objectives, focusing on different aspects of hand configuration. However, the lack of experimental comparative studies leaves the significance and effectiveness of these objectives unclear. This work aims to analyze these retargeting objectives for dexterous manipulation through extensive real-world comparative experiments. Specifically, we propose a comprehensive retargeting objective formulation that integrates intuitively crucial factors appearing in recent approaches. The significance of each factor is evaluated through experimental ablation studies on the full objective in kinematic posture retargeting and real-world teleoperated manipulation tasks. Experimental results and conclusions provide valuable insights for designing more accurate and effective retargeting algorithms for real-world dexterous manipulation.
Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Venue: arXiv cs.CR 2025
Year: 2025

Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, the newly created transactions for deferred payments introduce additional time overheads, which weaken the scalability of BNPL services. To address these issues, we propose an efficient and privacy-preserving blockchain-based asynchronous payment scheme (Epass), which has promising scalability while protecting the privacy of on-chain consumer transactions. Specifically, Epass leverages locally verifiable signatures to guarantee the privacy of consumer transactions against malicious acts. Then, a privacy-preserving asynchronous payment scheme can be further constructed by leveraging time-release encryption to control trapdoors of redactable blockchain, reducing time overheads by modifying transactions for deferred payment. We give formal definitions and security models, generic structures, and formal proofs for Epass. Extensive comparisons and experimental analysis show that \textsf{Epass} achieves KB-level communication costs, and reduces time overhead by more than four times in comparisons with locally verifiable signatures and Go-Ethereum private test networks.
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Venue: arXiv cs.DC 2025
Year: 2025

The growing gap between the increasing complexity of large language models (LLMs) and the limited computational budgets of edge devices poses a key challenge for efficient on-device inference, despite gradual improvements in hardware capabilities. Existing strategies, such as aggressive quantization, pruning, or remote inference, trade accuracy for efficiency or lead to substantial cost burdens. This position paper introduces a new framework that leverages speculative decoding, previously viewed primarily as a decoding acceleration technique for autoregressive generation of LLMs, as a promising approach specifically adapted for edge computing by orchestrating computation across heterogeneous devices. We propose \acronym, a framework that allows lightweight edge devices to draft multiple candidate tokens locally using diverse draft models, while a single, shared edge server verifies the tokens utilizing a more precise target model. To further increase the efficiency of verification, the edge server batch the diverse verification requests from devices. This approach supports device heterogeneity and reduces server-side memory footprint by sharing the same upstream target model across multiple devices. Our initial experiments with Jetson Orin Nano, Raspberry Pi 4B/5, and an edge server equipped with 4 Nvidia A100 GPUs indicate substantial benefits: 2.2 more system throughput, 2.8 more system capacity, and better cost efficiency, all without sacrificing model accuracy.
PGDA-KGQA: A Prompt-Guided Generative Framework with Multiple Data Augmentation Strategies for Knowledge Graph Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Knowledge Graph Question Answering (KGQA) is a crucial task in natural language processing that requires reasoning over knowledge graphs (KGs) to answer natural language questions. Recent methods utilizing large language models (LLMs) have shown remarkable semantic parsing capabilities but are limited by the scarcity of diverse annotated data and multi-hop reasoning samples. Traditional data augmentation approaches are focus mainly on single-hop questions and prone to semantic distortion, while LLM-based methods primarily address semantic distortion but usually neglect multi-hop reasoning, thus limiting data diversity. The scarcity of multi-hop samples further weakens models' generalization. To address these issues, we propose PGDA-KGQA, a prompt-guided generative framework with multiple data augmentation strategies for KGQA. At its core, PGDA-KGQA employs a unified prompt-design paradigm: by crafting meticulously engineered prompts that integrate the provided textual content, it leverages LLMs to generate large-scale (question, logical form) pairs for model training. Specifically, PGDA-KGQA enriches its training set by: (1) generating single-hop pseudo questions to improve the alignment of question semantics with KG relations; (2) applying semantic-preserving question rewriting to improve robustness against linguistic variations; (3) employing answer-guided reverse path exploration to create realistic multi-hop questions. By adopting an augment-generate-retrieve semantic parsing pipeline, PGDA-KGQA utilizes the augmented data to enhance the accuracy of logical form generation and thus improve answer retrieval performance. Experiments demonstrate that outperforms state-of-the-art methods on standard KGQA datasets, achieving improvements on WebQSP by 2.8%, 1.2%, and 3.1% and on ComplexWebQuestions by 1.8%, 1.1%, and 2.4% in F1, Hits@1, and Accuracy, respectively.
Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions. As the diffusion model has better training robustness than other generative models, it performs well in imitation learning through successful robot demonstrations. However, the diffusion-based policy methods typically require significant time to iteratively denoise robot actions, which hinders real-time responses in robotic manipulation. Moreover, existing diffusion policies model a time-varying action denoising process, whose temporal complexity increases the difficulty of model training and leads to suboptimal action accuracy. To generate robot actions efficiently and accurately, we present the Time-Unified Diffusion Policy (TUDP), which utilizes action recognition capabilities to build a time-unified denoising process. On the one hand, we build a time-unified velocity field in action space with additional action discrimination information. By unifying all timesteps of action denoising, our velocity field reduces the difficulty of policy learning and speeds up action generation. On the other hand, we propose an action-wise training method, which introduces an action discrimination branch to supply additional action discrimination information. Through action-wise training, the TUDP implicitly learns the ability to discern successful actions to better denoising accuracy. Our method achieves state-of-the-art performance on RLBench with the highest success rate of 82.6% on a multi-view setup and 83.8% on a single-view setup. In particular, when using fewer denoising iterations, TUDP achieves a more significant improvement in success rate. Additionally, TUDP can produce accurate actions for a wide range of real-world tasks.
Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
Venue: arXiv cs.CL 2025
Year: 2025

Supervised Fine-Tuning (SFT) is a critical step for enhancing the instruction-following capabilities of Large Language Models (LLMs) and adapting them to specialized domains. However, SFT often leads to a degradation of the model's general abilities, a phenomenon known as catastrophic forgetting. This problem is exacerbated when third-party practitioners fine-tune open-source models, as the original SFT data is typically not available. To address this challenge, we propose a novel and cost-effective SFT method that effectively mitigates catastrophic forgetting without requiring access to the original SFT data. Our approach first reconstructs the likely instruction distribution of the base model. It then employs a multi-model generation and filtering pipeline to synthesize a high-quality general-purpose dataset. This synthetic dataset is mixed with new, domain-specific data for fine-tuning. Experimental results show that our method not only preserves the model's capabilities in general domains but also improves task-specific performance, outperforming baselines that use publicly available SFT datasets.
A Novel Lightweight Transformer with Edge-Aware Fusion for Remote Sensing Image Captioning
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based models have achieved strong performance in remote sensing image captioning by capturing long-range dependencies and contextual information. However, their practical deployment is hindered by high computational costs, especially in multi-modal frameworks that employ separate transformer-based encoders and decoders. In addition, existing remote sensing image captioning models primarily focus on high-level semantic extraction while often overlooking fine-grained structural features such as edges, contours, and object boundaries. To address these challenges, a lightweight transformer architecture is proposed by reducing the dimensionality of the encoder layers and employing a distilled version of GPT-2 as the decoder. A knowledge distillation strategy is used to transfer knowledge from a more complex teacher model to improve the performance of the lightweight network. Furthermore, an edge-aware enhancement strategy is incorporated to enhance image representation and object boundary understanding, enabling the model to capture fine-grained spatial details in remote sensing images. Experimental results demonstrate that the proposed approach significantly improves caption quality compared to state-of-the-art methods.
ArcNeural: A Multi-Modal Database for the Gen-AI Era
Venue: arXiv cs.DB 2025
Year: 2025

ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integration of transaction and analytical processing. Experimental evaluations demonstrate ArcNeural's superior performance and scalability compared to state-of-the-art systems. This system bridges structured and unstructured data management, offering a versatile solution for enterprise-grade AI applications. ArcNeural's design addresses the challenges of multimodal data processing, providing a robust framework for intelligent, data-driven solutions in the Gen AI era.
WakeupUrban: Unsupervised Semantic Segmentation of Mid-20$^{th}$ century Urban Landscapes with Satellite Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Historical satellite imagery archive, such as Keyhole satellite data, offers rare insights into understanding early urban development and long-term transformation. However, severe quality degradation ($\textit{e.g.}$, distortion, misalignment, and spectral scarcity) and the absence of annotations have long hindered its analysis. To bridge this gap and enhance understanding of urban development, we introduce $\textbf{WakeupUrbanBench}$, an annotated segmentation dataset based on historical satellite imagery with the earliest observation time among all existing remote sensing (RS) datasets, along with a framework for unsupervised segmentation tasks, $\textbf{WakeupUSM}$. First, WakeupUrbanBench serves as a pioneer, expertly annotated dataset built on mid-$20^{\text{th}}$ century RS imagery, involving four key urban classes and spanning 4 cities across 2 continents with nearly 1000 km$^2$ area of diverse urban morphologies, and additionally introducing one present-day city. Second, WakeupUSM is a novel unsupervised semantic segmentation framework for historical RS imagery. It employs a confidence-aware alignment mechanism and focal-confidence loss based on a self-supervised learning architecture, which generates robust pseudo-labels and adaptively prioritizes prediction difficulty and label reliability to improve unsupervised segmentation on noisy historical data without manual supervision. Comprehensive experiments demonstrate WakeupUSM significantly outperforms existing unsupervised segmentation methods $\textbf{both WakeupUrbanBench and public dataset}$, promising to pave the way for quantitative studies of long-term urban change using modern computer vision. Our benchmark and codes will be released at https://github.com/Tianxiang-Hao/WakeupUrban.
Advances on Affordable Hardware Platforms for Human Demonstration Acquisition in Agricultural Applications
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents advances on the Universal Manipulation Interface (UMI), a low-cost hand-held gripper for robot Learning from Demonstration (LfD), for complex in-the-wild scenarios found in agricultural settings. The focus is on improving the acquisition of suitable samples with minimal additional setup. Firstly, idle times and user's cognitive load are reduced through the extraction of individual samples from a continuous demonstration considering task events. Secondly, reliability on the generation of task sample's trajectories is increased through the combination on-board inertial measurements and external visual marker localization usage using Extended Kalman Filtering (EKF). Results are presented for a fruit harvesting task, outperforming the default pipeline.